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Superconducting Qubits: Latest IBM, Google & Rigetti Developments

Latest superconducting qubit news: IBM Quantum, Google Willow chip, Rigetti Novera. Cryogenic systems, error correction & quantum supremacy updates.

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Superconducting qubits represent the most commercially advanced quantum computing technology, powering systems from IBM, Google, and Rigetti. These quantum processors leverage Josephson junctions—superconducting circuits that create non-linear inductance—to generate controllable quantum states at temperatures near absolute zero (15-20 millikelvin).

The dominant superconducting qubit design, the transmon qubit, balances coherence time and control simplicity by reducing sensitivity to charge noise. Recent breakthroughs include Google's Willow chip achieving below-threshold quantum error correction, demonstrating that increasing qubit count can actually reduce errors—a critical milestone for fault-tolerant quantum computing. IBM continues scaling its Heron processor architecture toward 1,000+ qubit systems while improving gate fidelities above 99.5%.

India's National Quantum Mission & Superconducting Qubits

India's National Quantum Mission (NQM), approved by the Union Cabinet on 19 April 2023 with an allocation of ₹6,003.65 crore for eight years (2023-2031), prioritizes superconducting qubit development under its Quantum Computing Thematic Hub. The Foundation for QC Innovation at IISc Bengaluru serves as the lead institution for this hub, working with IIT Delhi, IIT Bombay, TIFR Mumbai, and other institutions. The Tata Institute of Fundamental Research (TIFR) in Mumbai has established dilution refrigeration laboratories capable of operating at ultra-low temperatures to support superconducting qubit research. In August 2024, DRDO scientists from the Young Scientists Laboratory for Quantum Technologies (DYSL-QT), in collaboration with TIFR and TCS, completed end-to-end testing of a 6-qubit superconducting quantum processor with a novel ring-resonator design. This system includes a cloud-based interface developed by TCS for submitting quantum circuits and receiving computed results.

The NQM targets developing intermediate-scale quantum computers with 50-1000 physical qubits in eight years using various platforms including superconducting and photonic technology. Indigenous development of quantum fabrication facilities is underway, with IISc Bengaluru and IIT Bombay establishing quantum computing fabrication facilities under a ₹720 crore investment announced in November 2025. These facilities will support superconducting, photonic, and spin qubit technologies.

Key Advantages

Key advantages of superconducting qubits include nanosecond gate speeds enabling rapid algorithm execution, established semiconductor fabrication processes supporting manufacturing scalability, and a strong cryogenic infrastructure ecosystem. Current challenges include decoherence times (100-300 microseconds) that remain shorter than trapped-ion alternatives, error rates requiring extensive quantum error correction overhead, and cryogenic operation demands for specialized infrastructure.

Major Players

Major global players include IBM Quantum with cloud-accessible systems (Eagle, Osprey, Condor processors), Google Quantum AI focusing on error correction and quantum supremacy demonstrations, and Rigetti Computing offering hybrid quantum-classical systems. In India, the Foundation for QC Innovation at IISc, TIFR Mumbai, and IIT Bombay are building national capability with NQM support, while startups including QpiAI India are working on superconducting quantum computers.

IonQ vs. Quantum Computing Inc.: Which Quantum Computing Stock Is a Better Buy in 2026?quantum-computing

IonQ vs. Quantum Computing Inc.: Which Quantum Computing Stock Is a Better Buy in 2026?

As the race for quantum supremacy intensifies, choosing between IonQ (IONQ -7.25%) and Quantum Computing Inc. (QUBT -6.78%), which refers to itself as QCi, requires a careful look at their vastly different scales and unique hardware approaches.IonQ uses trapped-ion technology to build systems accessible through major cloud platforms, while QCi focuses on photonic chips and room-temperature hardware. Both companies represent high-risk, high-reward plays in a nascent industry where long-term commercial viability remains the primary hurdle for investors to consider.The case for IonQIonQ specializes in developing quantum hardware using trapped ions. The company primarily sells access to its systems through the cloud computing ecosystem, partnering with giants such as Amazon-owned AWS. Revenue concentration remains a risk, as the company is heavily reliant on a small number of major customers, and customer concentration like this adds a layer of risk to the business.In its latest annual report, filed for fiscal year (FY) 2025, revenue reached $130 million, representing a significant jump of 202% compared to the previous year. Despite this growth, the company reported a net loss of $510.4 million for the period. This widening loss is common in the early stages of capital-intensive hardware development, though the triple-digit top-line growth suggests increasing demand for its trapped-ion systems among commercial and research clients.As of its December 2025 balance sheet, the company's debt-to-equity ratio is zero, which means total debt is negligible relative to its shareholder equity. The current ratio stands at 15.5x, a measure of its ability to cover short-term debts with assets that can be converted to cash within a year. Free cash flow, which is cash from operations minus capital expenditures, was a negative $299.6 million in FY 2025, reflecting high costs of building out its infrastructure.The case for Quantum Computing Inc.According to its latest annual report for

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Japan Operationalizes First Full-Stack Neutral-Atom Quantum Computer “Shunkai”quantum-computing

Japan Operationalizes First Full-Stack Neutral-Atom Quantum Computer “Shunkai”

Japan Operationalizes First Full-Stack Neutral-Atom Quantum Computer “Shunkai” The Institute for Molecular Science (IMS), part of Japan’s National Institutes of Natural Sciences (NINS), has announced that Japan’s first full-stack neutral-atom quantum computer, named “Shunkai” (春海), is now operational. Led by Project Manager Professor Kenji Ohmori under Goal 6 of the Japanese Cabinet Office / JST Moonshot Research and Development Program, the platform was built through a industry-academia consortium partnering with Hitachi, Ltd. for the software stack and Infleqtion, Inc. for the Quantum Processing Unit (QPU) hardware stack. [ IMS Neutral-Atom System Architecture: “Shunkai” ]Hardware Stack (QPU)Software & Control StackScale & Roadmap Targets• Neutral Rubidium Atoms• Hitachi Software Stack• Phase 1: 50 Physical Qubits• Optical Tweezer Arrays• Infleqtion QPU Electronics• Phase 2: 500 Physical Qubits• Room-Temp Qubit Control• Dynamically Moved Atoms• 2031 Target: 10k FTQC Qubits Full-Stack Integration and Optical Tweezer Control The “Shunkai” system is named after Harumi (Shunkai) Shibukawa, the Edo-period astronomer who designed Japan’s first indigenous calendar based on celestial calculations. The full-stack platform integrates user-level software directly down to physical laser control and readout systems: Optical Tweezer Qubit Trapping: Single neutral atoms are trapped in a two-dimensional grid using optical tweezers created by tightly focused laser beams through high-NA objective lenses. Quantum logic gates are driven via targeted microwave and laser pulses, with individual readout executed via high-resolution fluorescence cameras. Room-Temperature Reconfiguration: Operating without cryogenic dilution refrigerators, the platform leverages dynamic atom transport to physically move qubits during runtime, enabling all-to-all connectivity and reconfigurable circuit topologies. Consortium Ecosystem: Hardware component integration and QPU packaging were developed

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Quantum Computing Stocks IonQ, Rigetti Computing, and D-Wave Quantum Have Put Wall Street on Notice With This $863 Million Warningquantum-computing

Quantum Computing Stocks IonQ, Rigetti Computing, and D-Wave Quantum Have Put Wall Street on Notice With This $863 Million Warning

Although artificial intelligence has been driving Wall Street's bull market for almost four years, it's not the only game-changing trend that's capturing the attention and capital of investors. The quantum computing revolution is a potential trillion-dollar addressable market and is exciting investors. As of October 2025, several pure-play quantum computing stocks were delivering breakneck trailing 12-month (TTM) returns. IonQ (IONQ +8.02%), Rigetti Computing (RGTI +11.48%), and D-Wave Quantum (QBTS +8.46%) gained as much 6,200% over the trailing year. Investors who had the wherewithal to put their capital to work in these pure-play companies have been handsomely rewarded. Image source: Getty Images. But things may not be as perfect as the eye-popping two-year gains in quantum computing stocks suggest. Based on the actions of those who know IonQ, Rigetti, and D-Wave best, a worrisome message has been sent to Wall Street. Insiders at IonQ, Rigetti, and D-Wave have put Wall Street on notice Even though dozens of analysts closely monitor these pure-play quantum computing stocks, no one understands the nuts and bolts of these companies better than their insiders. An "insider" is a high-ranking executive, board member, or beneficial owner of at least 10% of a company's outstanding shares who may possess non-public information. Typically, insiders are a public company's biggest cheerleaders. But sometimes their actions speak louder than words. Securities law requires that insiders report any purchases or sales in their company's stock (including option exercises) via Form 4 within two business days. This also allows everyday investors to track whether insiders have been buyers or sellers of their company's stock. ExpandNYSE: IONQIonQToday's Change(8.02%) $3.33Current Price$44.86Key Data Points*:nth-last-child(-n+2)]:border-b-0">Market Cap$18BMarket cap calculated using publicly traded shares outstanding only. Does not include unlisted, private, or dual-class non-traded sh

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To Scale Up or To Scale Out: Evaluating Space-Time Costs of Compiled Logical Circuits on Modular Superconducting Quantum Processorsquantum-computing

To Scale Up or To Scale Out: Evaluating Space-Time Costs of Compiled Logical Circuits on Modular Superconducting Quantum Processors

--> Quantum Physics arXiv:2608.20462 (quant-ph) [Submitted on 20 Aug 2026] Title:To Scale Up or To Scale Out: Evaluating Space-Time Costs of Compiled Logical Circuits on Modular Superconducting Quantum Processors Authors:Nikiforos Paraskevopoulos, Sebastian de Bone, Mick Christophersen, Simon Storz, A. Mert Bozkurt, Arno Bargerbos, Sebastian Feld View a PDF of the paper titled To Scale Up or To Scale Out: Evaluating Space-Time Costs of Compiled Logical Circuits on Modular Superconducting Quantum Processors, by Nikiforos Paraskevopoulos and 6 other authors View PDF HTML (experimental) Abstract:Modular integration has emerged as the main pathway for scaling superconducting quantum processing units (QPUs) beyond the constraints of fabrication yield and physical footprint. Currently, two primary strategies lead this effort. Mirroring the "Scaling Up" and "Scaling Out" approaches in GPU architectures and AI infrastructures, these are: chiplet-based scaling, which preserves dense connectivity and high gate fidelity at the expense of engineering complexity, and distributed architectures, which decouple system scaling from monolithic QPU advancements at the expense of sparser connectivity and lower interconnect quality. To evaluate these approaches, we introduce a quantitative stress test measuring the execution cost of a dense workload of random logical entangling operations using a surface code scheme. Using a dedicated compiler, we compute the space-time cost as the number of network nodes increases, analysing this scaling behaviour across various surface code distances, Bell-state fidelities, and Bell-pair generation times. We find that distributed architectures incur an up to exponential space-time performance penalty compared to an effectively monolithic architecture across all simulations. Our results also show that as the network grows, this penalty manifests in two distinct scaling regimes: a noise-dominated regime constrained by insufficient Bell-state fidelity an

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Quantum phase estimation for nondestructive monitoring and Wigner tomography of bosonic fieldsquantum-computing

Quantum phase estimation for nondestructive monitoring and Wigner tomography of bosonic fields

--> Quantum Physics arXiv:2608.20787 (quant-ph) [Submitted on 21 Aug 2026] Title:Quantum phase estimation for nondestructive monitoring and Wigner tomography of bosonic fields Authors:Lucas R. S. Santos, Ciro M. Diniz, Daniel Z. Rossatto, Celso J. Villas-Boas View a PDF of the paper titled Quantum phase estimation for nondestructive monitoring and Wigner tomography of bosonic fields, by Lucas R. S. Santos and 3 other authors View PDF HTML (experimental) Abstract:Quantum phase estimation is usually introduced as an algorithmic primitive for extracting eigenphases of unitary operators. Here we show that, when implemented through a dispersive light-matter interaction, it can also be used as a nondestructive measurement tool for bosonic fields. We consider a bosonic mode coupled to a multi-qubit register and calibrate the photon-number dependent phase shifts so that the register performs a number-resolved quantum phase estimation readout. Repeating this readout during dissipative evolution enables nondestructive monitoring of photon-number dynamics. We then show that the same readout can be converted into a Wigner tomography reconstruction by applying phase-space displacements before the quantum phase estimation block. Numerical reconstructions for Fock, coherent, and even/odd Schrödinger cat states show the expected nonclassical phase-space structures and near-unity Wigner overlap fidelities. The protocol provides a unified route to nondestructive monitoring and state tomography of bosonic fields, with direct relevance for bosonic-state characterization, calibration, and control in superconducting quantum architectures. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2608.20787 [quant-ph]   (or arXiv:2608.20787v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2608.20787 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Lucas Santos [view email] [v1] Fri, 21 Aug 2026 06:59:17 UTC (2,5

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Rigetti Computing's COO Sells Over 9,000 Company Shares. What Does That Mean for Investors?quantum-computing

Rigetti Computing's COO Sells Over 9,000 Company Shares. What Does That Mean for Investors?

David Rivas, Chief Operating Officer of Rigetti Computing, Inc. (RGTI +11.48%), sold 9,038 shares of common stock on August 20, 2026 per the SEC Form 4 filing.Transaction summaryMetricValueTransaction value~$152,000Shares sold (directly held)9,038Post-transaction shares (directly held)316,907Post-transaction value$5.1 millionTransaction value based on SEC Form 4 weighted average sale price ($16.79); post-transaction value based on August 20, 2026 market close ($16.07).Key questionsWhat was the primary driver of this insider sale?The sale was non-discretionary, executed solely to satisfy tax withholding obligations triggered by the settlement of restricted stock units (RSUs). This type of transaction is part of the insider's existing equity compensation structure and does not represent a discretionary market trade or a change in investment thesis.How does the insider's remaining stake compare to the company's capital structure?Rivas continues to hold 316,907 shares directly, representing a 0.0953% ownership interest in the company. This remaining equity position maintains the insider's alignment with shareholder interests following the automatic tax-related disposal.What was the market environment at the time of the transaction?The shares were priced at $16.07 at the August 20, 2026 market close, which followed a 9% one-year total return as of the transaction date. The company, which operates as a full-stack quantum computing company, currently maintains a market cap of $5.7 billion.Company OverviewMetricValueShare Price (as of market close 2026-08-19)$17.00Market Capitalization$5.7 billionRevenue (TTM)$13.4 millionNet Income (TTM)-$238.7 millionCompany SnapshotRigetti Computing develops and manufactures full-stack quantum computing systems, including superconducting quantum processors, and provides access to these systems through its Quantum Cloud Services platform across public, private, and hybrid cloud environments.The company generates revenue through quantum co

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Michigan Team Shapes Resonator Spectra with Dual Interferencequantum-computing

Michigan Team Shapes Resonator Spectra with Dual Interference

P. K. Rath from the Indian Association for the Cultivation of Science and colleagues report a new method for shaping the spectral response of gigahertz-frequency surface acoustic wave resonators by simultaneously introducing both electromagnetic and acoustic Fano interference. Systematic modification of resonator acoustic reflectivity allowed isolation and analysis of each interference mechanism independently. The broad operating temperature range, from ambient to cryogenic temperatures, highlights potential applications in both classical and quantum sensing. Surface acoustic waves (SAWs) represent mechanical oscillations travelling along a crystal’s surface, localised approximately one wavelength above and below it. These waves are generated on piezoelectric crystals via time-varying electric fields applied to metallic transducers converting electrical signals into mechanical waves. Owing to their strain and piezoelectric coupling, SAWs provide a flexible platform for controlling and probing many condensed-matter systems. SAW techniques have been widely used to probe frequency-dependent conductivity in low-dimensional many-body quantum matter and create tunable acoustic lattices for manipulating collective states. They enable coherent transport of individual charges, spins, and single photon generation. Integration with two-dimensional electronic systems has investigated high-frequency acoustically driven transport and band structure engineering. Beyond condensed matter physics, SAW devices operating in the MHz to GHz range are ubiquitous in RF and microwave signal processing functioning as filters, delay lines, and resonators. Moreover, since SAWs propagate on substrate surfaces they exhibit key sensitivity to minute perturbations due to external factors such as pressure and temperature. Simultaneous electromagnetic and acoustic Fano interference boosts resonator sensitivity six-fold A six-fold enhancement in sensitivity was achieved by simultaneously introducing

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Who’s News: Strategic Appointments at D-Wave, BTQ Technologies, Symmatrics, and Rigetti Computingquantum-computing

Who’s News: Strategic Appointments at D-Wave, BTQ Technologies, Symmatrics, and Rigetti Computing

Who’s News: Strategic Appointments at D-Wave, BTQ Technologies, Symmatrics, and Rigetti Computing D-Wave Quantum Inc. has appointed Kevan P. Krysler to its Board of Directors and Audit Committee. Krysler currently serves as Chief Financial Officer of Carbon Robotics and brings over two decades of financial leadership experience from previous executive roles, including Chief Financial Officer of Everpure, Inc., and Senior Vice President of Finance and Chief Accounting Officer at VMware, Inc. His appointment supports D-Wave’s strategic growth objectives and corporate governance as the company scales its commercial adoption of annealing and gate-model quantum technologies. The full official release is available here. BTQ Technologies Corp. has appointed Dr. Michael Grace to its U.S. technical team to support the development and commercialization of its Quantum Compute-in-Memory (QCIM) architecture. Dr. Grace previously led the Knox Security Team at Samsung and served as Director of Product Security at Mojo Vision. His appointment coincides with the advancement of BTQ’s QCIM core through its module-level integration and validation phase, following successful functional verification of its 28-nanometre design environment alongside the Industrial Technology Research Institute (ITRI). The complete announcement can be found here. Symmatrics has expanded its executive leadership team with two key appointments. The company has named Jim Garrity as Senior Vice President of Growth to manage commercial expansion, strategic partnerships, and go-to-market strategies for its one-time pad symmetric key delivery platform. Garrity brings over 30 years of channel and sales leadership experience, having previously held senior roles at MoreDirect, VMware, Citrix, and Insight. The news release details are available here. Additionally, Symmatrics has appointed Joe Reddix as Vice President of Federal Sector to lead its federal growth strategy and pilot deployments across government agencies

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New estimates From Google Quantum AI show quantum attack on Bitcoin is closer than thoughtquantum-computing

New estimates From Google Quantum AI show quantum attack on Bitcoin is closer than thought

Google Quantum AI researchers have determined that breaking the core cryptography of various cryptocurrencies, secured by the secp256k1 curve, may require as few as 1200 logical qubits and 90 million Toffoli gates, a significantly lower threshold than previously understood. The team’s work elucidates specific vulnerabilities blockchain technologies face with the development of quantum computers and potential mitigation strategies. To ensure responsible disclosure, the researchers validated their findings using a zero-knowledge proof without revealing specific attack vectors. This analysis reveals that emerging “fast-clock” quantum computers could enable attacks on cryptocurrency transactions in the public mempool. Shor’s Algorithm Estimates for secp256k1 Bitcoin Attacks This represents a significant reduction in the estimated resources needed for a successful attack compared to earlier projections, bringing the threat of quantum decryption closer to reality. These architectures, the researchers note, could enable “on-spend” attacks targeting public mempool transactions, potentially allowing malicious actors to seize funds before they are confirmed on the blockchain. A key distinction highlighted in the analysis is the difference between fast-clock and “slow-clock” quantum computers, such as those based on neutral atoms or ion traps. The researchers found that circuits executing Shor’s algorithm on superconducting architectures, with a 10-3 physical error rate and planar connectivity, could complete the calculation in minutes using fewer than half a million physical qubits. This speed is critical because it suggests a viable attack window exists once sufficiently powerful quantum computers become available. The implications extend beyond Bitcoin, encompassing any cryptocurrency reliant on the secp256k1 curve for securing transactions. Technical solutions would benefit from accompanying public policy, and highlight ongoing efforts to transition to Post-Quantum Cryptog

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Power-Law Tails Signal Semi-Fractal States on Chiral Cayley Treesquantum-computing

Power-Law Tails Signal Semi-Fractal States on Chiral Cayley Trees

Google Quantum AI researchers have reported a surprising distribution of local density of states, specifically, broad power-law tails, in the behavior of quantum particles moving on a Cayley tree, suggesting a novel wave-function statistic they term semi-fractality. This differs from standard fractal behavior, representing an intermediate state between fully extended and localized quantum states, achieved by designing a model where particle movement is heavily influenced by connections with diminished strength. The work demonstrates that as the exponent controlling the power-law hopping distribution is changed, the system transitions from a semi-fractal regime to a localized one. Researchers found this represents an extreme intermediate form of quantum state, challenging the traditional understanding of how wave functions behave in non-ergodic systems. Carlo Vanoni of Princeton University, Vladimir E. Kravtsov of The Abdus Salam ICTP, and Boris L. Altshuler of Columbia University detailed their findings in recent work, focusing on a quantum particle traversing an infinite Cayley tree where the strength of connections between nodes varies significantly. The researchers deliberately designed the model with a distribution of hopping amplitudes to explore unusual quantum phenomena. Exact diagonalization further revealed a hierarchy of eigenstate weights, supporting the interpretation of semi-fractality as a consequence of this weight distribution. Researchers are increasingly focused on quantum states that defy easy categorization, and work with Cayley trees, infinitely branching structures, is revealing a particularly subtle case. This setup allows researchers to observe how the particle’s movement is heavily influenced by these diminished connections. The team’s analysis reveals that the system occupies an extensive portion of the tree, yet its higher-order moments behave as if it were a multifractal state, a complex interplay of extension and localization. The quest

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Quantum X Labs Outperforms PyMatching Benchmarks on Google Quantum Hardware Surface-Code Dataset Using NVIDIA CUDA-Qquantum-computing

Quantum X Labs Outperforms PyMatching Benchmarks on Google Quantum Hardware Surface-Code Dataset Using NVIDIA CUDA-Q

Quantum X Labs Outperforms PyMatching Benchmarks on Google Quantum Hardware Surface-Code Dataset Using NVIDIA CUDA-Q Quantum software developer Quantum X Labs Inc. (Nasdaq: QXL) has announced new performance results from its AI-driven quantum error correction (QEC) decoder program. Testing its updated model against Google’s public surface-code experimental dataset, Quantum X Labs demonstrated improved decoding accuracy compared to standard matching-family baselines—including Google’s published correlated-matching and PyMatching benchmark results for the same surface-code configuration. [ Quantum X Labs AI-QEC Decoder Architecture ] │ ┌──────────────────────────────────┴──────────────────────────────────┐ ▼ ▼ Synthetic AI Training Pipeline Real-Hardware Syndrome Generalization • Trained Exclusively on Synthetic Samples. • Tested on Google Surface-Code Experimental Dataset. • Syndrome Info & Error Weighting Integration. • Outperforms PyMatching & Correlated-Matching. • NVIDIA CUDA-Q & GPU Acceleration. • Low-Latency Foundation for Real-Time QEC. Synthetic-to-Real Generalization and GPU Acceleration A central challenge in real-time quantum error correction is developing decoders that can interpret physical syndrome data rapidly without incurring prohibitive computational latency or requiring extensive retraining on real hardware shots: Zero-Shot Real Hardware Generalization: QXL’s updated AI decoder model was trained exclusively on synthetic simulation samples and was not exposed to real hardware shots during training. Achieving higher accuracy than standard minimum-weight perfect matching (MWPM) solvers on real experimental data confirms the model’s ability to generalize to physical device noise. GPU-Accelerated Integration: Built to leverage NVIDIA CUDA-Q and accelerated GPU computing architectures, the decoder combines surface-code topological structures with AI-based error weighting to provide a low-latency path toward real-time decoding on fault-tolera

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Leeds Team Simulates Black Hole Interiors with Qubitsquantum-computing

Leeds Team Simulates Black Hole Interiors with Qubits

Both Hawking thermality and interior quantum scrambling have been successfully simulated within a unified framework on superconducting quantum hardware. A chiral spin chain implemented on this platform at the University of Leeds revealed an inverse relationship between peak arrival time and surface gravity, establishing a calibrated estimator for Hawking temperature. Key black hole characteristics are now unifiedly simulated using superconducting quantum hardware. The simulation combines observations of Hawking radiation with internal chaotic behaviour known as scrambling within one experiment. Previously, simulations typically studied either emission or chaos separately; this approach enables fuller exploration of connections between gravity and quantum mechanics. The University of Leeds has achieved an advance by simulating key characteristics of black holes, Hawking thermality and internal quantum scrambling, within a single experiment on superconducting hardware. Their setup utilises a simplified mathematical representation of a black hole’s edge; this ‘chiral spin chain’ consists of interconnected quantum bits behaving like tiny magnets with a specific directional twist. Researchers observed Hawking thermality alongside investigation into how information becomes scrambled within the simulated black hole’s interior, akin to repeatedly shuffling cards until their original order is lost. This unified approach allows for deeper exploration of connections between gravity and quantum mechanics than previous simulations which focused on either emission or chaos in isolation; detailed technical aspects regarding circuit implementation and model parameters follow. Simulating Black Hole Horizons with Programmable Superconducting Qubits A technique centred around a ‘chiral spin chain’, a simplified mathematical representation of the edge of a black hole built from interconnected quantum bits behaving like tiny magnets with a specific directional twist, enabled precise con

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Researchers Build Adaptive Quantum Sensor Designs with Reinforcement Learningquantum-computing

Researchers Build Adaptive Quantum Sensor Designs with Reinforcement Learning

A new set of tools called AUTOQSENSE addresses challenges in high-precision parameter estimation where performance is key to quantum circuit architecture during probe preparation and measurement periods. The method optimises continuous parameters within pre-defined ansatzes, restricting the explored design space and hindering adaptability to specific sensing tasks and hardware constraints periods. Jie Liu and Xin Wang at the University of Science and Technology of China present a reinforcement-learning framework designed to search for optimal circuit architectures using Fisher-information-based objectives periods. In few-qubit systems, an agent sequentially constructs both preparation and measurement circuits periods. For larger systems, a distributed formulation assigns local circuit design responsibilities to subsystem agents and establishes inter-block communication protocols periods. Automated circuit design enhances parameter estimation with reduced gate complexity Entangling gate counts decreased by up to 30% compared to established hardware-efficient approaches while maintaining precise parameter estimation periods. This improvement unlocks previously unattainable sensing protocols due to resource limitations. Conventional methods struggle when faced with complex noise models or large numbers of qubits requiring extensive optimisation periods. textsc{AutoQSense}, a new framework from David Hayes and his team alongside collaborators Quantum AI, automatically designs optimal circuits for quantum sensors using reinforcement learning, a technique where an agent learns through trial and error, and Fisher information, which measures data gained from each measurement period. The system successfully rediscovers known strategies whilst adapting effectively to dephasing noise, a common source of errors in quantum systems, demonstrating its flexible application across diverse scenarios periods. Achieving superior results on simulations involving up to four qubits was ve

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Quantum algorithm solves matrix equations much faster than classical methodsquantum-computing

Quantum algorithm solves matrix equations much faster than classical methods

Rolando D. Somma of Google Quantum AI and colleagues have devised a quantum algorithm that efficiently solves the Sylvester equation, a fundamental linear matrix equation used in fields from control theory to physics. The approach constructs a solution matrix using a technique, allowing for faster access to its properties than traditional methods of preparing a quantum state. The query and gate complexities of the quantum circuit that implements this block-encoding are almost linear in a condition number that depends on the input matrices and logarithmically with the problem’s dimension and desired accuracy. The team demonstrates this circuit can efficiently tackle problems within the BQP class, suggesting a pathway toward practical quantum solutions for complex linear algebra. Quantum Algorithm for the Sylvester Equation Google Quantum AI researchers have devised a quantum circuit capable of solving the Sylvester equation with computational demands scaling favorably with problem size. Somma and colleagues, centers on constructing a block-encoding of the solution matrix, offering a potential pathway to exponential speedups in instances where the condition number scales polylogarithmically with the problem size. Unlike traditional approaches that treat matrix equations as systems of linear equations with extremely large dimensions, this quantum algorithm employs specialized techniques tailored to directly construct the solution matrix. The core of this advancement lies in the algorithm’s efficiency in accessing properties of the solution matrix’s entries, achieving this faster than preparing the matrix as a quantum state. This is accomplished through a block-encoding, a unitary transformation where the first block represents the solution matrix, normalized by a rescaling factor, x. The query and gate complexities of the resulting quantum circuit are almost linear in a condition number, denoted as κ, which depends on the input matrices, and scale logarithmically with

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QOBLIB gains quantum optimization data from JIJquantum-computing

QOBLIB gains quantum optimization data from JIJ

JIJ is now contributing benchmark results to QOBLIB, an open library evaluating both quantum and classical optimization methods, signaling a push for standardized comparisons across the field, the company says. The company benchmarked its quantum optimization method using a subset of problems from QOBLIB and shared the data with the IBM Quantum team, who then referenced JIJ in a recent technical blog. “Shared benchmarks such as QOBLIB provide a common basis for comparing optimization methods,” JIJ stated, emphasizing the importance of transparent evaluation as it continues developing its technology within the broader quantum optimization community. JIJ Benchmarks Quantum Optimization Method with QOBLIB Datasets IBM Quantum recently highlighted JIJ’s contribution in a technical blog post detailing QOBLIB, publicly acknowledging the new benchmark submissions. This recognition indicates IBM is actively monitoring JIJ’s progress in quantum optimization and values the transparency offered by shared benchmarking datasets. JIJ intends to continue refining its technology and participating in open benchmarking initiatives within the quantum optimization community, according to QOBLIB. The OMMX Quantum Benchmarks repository currently includes a subset of problems sourced from QOBLIB, furthering the availability of standardized datasets for researchers. Utilizing these benchmarks is an important step in understanding the performance characteristics of its method through transparent, comparable evaluation, according to the company. Source: https://www.j-ij.com/en/news/20260821 Stay currentSee today’s quantum computing news on Quantum Zeitgeist for the latest breakthroughs in qubits, hardware, algorithms, and industry deals. Tags: Ivy Delaney Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity t

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The HALO Engine: $\mathcal{O}(1)$-Step Compilation and Localized String Rupture for Lattice Gauge Theories on Quantum Hardwarequantum-computing

The HALO Engine: $\mathcal{O}(1)$-Step Compilation and Localized String Rupture for Lattice Gauge Theories on Quantum Hardware

--> Quantum Physics arXiv:2608.19243 (quant-ph) [Submitted on 14 Aug 2026] Title:The HALO Engine: $\mathcal{O}(1)$-Step Compilation and Localized String Rupture for Lattice Gauge Theories on Quantum Hardware Authors:Abhiroop Gohar View a PDF of the paper titled The HALO Engine: $\mathcal{O}(1)$-Step Compilation and Localized String Rupture for Lattice Gauge Theories on Quantum Hardware, by Abhiroop Gohar View PDF HTML (experimental) Abstract:Simulating the real-time dynamics of lattice gauge theories (LGTs) represents a challenge for near-term quantum computing. Standard digital simulations rely on Trotterization schemes where circuit depth scales proportionally with lattice size, inevitably colliding with the coherence limits of noisy intermediate-scale quantum (NISQ) hardware. To deal with this depth-scaling bottleneck, we introduce the Hardware-Aware Lattice Optimization (HALO) compiler, an architecture that executes global time-evolution steps in an immutable $\mathcal{O}(1)$ circuit depth per Trotter step. Leveraging this framework, we elevate the digital simulation of the Quantum Link Model (QLM) truncation of the Schwinger model to the mesoscopic scale, utilizing a composite multi-qubit gauge link representation to support non-trivial electric field dynamics. We initialize and execute the non-perturbative dynamics of a heavily stretched $L=15$ meson string on a 16-qubit superconducting transmon processor. By coupling the $\mathcal{O}(1)$ compilation with Zero-Noise Extrapolation (ZNE), we suppress physical hardware decoherence to extract the precise dynamical crossover of localized pair creation, identifying the topological transition at $t \approx 0.790$ lattice units with an $18.3 \pm 2.2\%$ rupture probability. Furthermore, we empirically map the dynamical phase diagram of the mesoscopic lattice, pinpointing the effective confinement phase boundary at precisely $g_c = 1.0$. Finally, we extend the mathematical principles of the HALO engine to higher dimensi

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Simulating Black Hole Thermality and Interior Scrambling on a Superconducting Quantum Processorquantum-computing

Simulating Black Hole Thermality and Interior Scrambling on a Superconducting Quantum Processor

--> Quantum Physics arXiv:2608.19318 (quant-ph) [Submitted on 19 Aug 2026] Title:Simulating Black Hole Thermality and Interior Scrambling on a Superconducting Quantum Processor Authors:Ryan Smith, Ewan Forbes, Iason Sofos Andrew Hallam, Jiannis Pachos View a PDF of the paper titled Simulating Black Hole Thermality and Interior Scrambling on a Superconducting Quantum Processor, by Ryan Smith and 3 other authors View PDF Abstract:We implement a chiral spin-chain black hole simulator on IBM superconducting quantum hardware and probe, within a common microscopic framework, both semiclassical horizon physics and interacting quantum scrambling. We first measure the dispersion relation across the exterior, horizon and over-tilted interior regimes, reproducing the predicted evolution of the effective light-cone structure. To probe Hawking thermality, we prepare a localised excitation inside the horizon and monitor its density response at an exterior site, observing the predicted inverse relation between the peak arrival time and the surface gravity, thereby establishing a calibrated dynamical estimator of the Hawking temperature. Beyond the semiclassical regime, we continuously tune the interactions and distinguish non-exponential operator spreading in the free-fermion limit from Lyapunov-like OTOC decay in the strongly interacting chiral regime. These measurements use observable-specific Floquet circuits derived from the same parent chiral model, including its mean-field and coordinate-equivalent XY descriptions, to reduce circuit depth while preserving the physics relevant to each probe. Our results provide a unified programmable platform for studying horizon geometry, Hawking thermality and interacting scrambling on quantum hardware. Comments: Subjects: Quantum Physics (quant-ph); Strongly Correlated Electrons (cond-mat.str-el); High Energy Physics - Theory (hep-th) Cite as: arXiv:2608.19318 [quant-ph]   (or arXiv:2608.19318v1 [quant-ph] for this version)   htt

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Glassy dynamics with softened kinetic constraints on a noisy quantum computerquantum-computing

Glassy dynamics with softened kinetic constraints on a noisy quantum computer

--> Quantum Physics arXiv:2608.19335 (quant-ph) [Submitted on 19 Aug 2026] Title:Glassy dynamics with softened kinetic constraints on a noisy quantum computer Authors:Marcel Cech, Igor Lesanovsky, Federico Carollo View a PDF of the paper titled Glassy dynamics with softened kinetic constraints on a noisy quantum computer, by Marcel Cech and 2 other authors View PDF HTML (experimental) Abstract:Mid-circuit measurements provide direct access to trajectory-level observables, revealing dynamical structures in many-body systems that are invisible in ensemble-averaged quantities. We exploit this capability to realize and study an instance of the Floquet-East model on a superconducting quantum processor. Here, the combination of mid-circuit measurements, kinetically constrained unitary operations and hardware noise gives rise to intricate many-body phenomena. Analyzing trajectories obtained from temporally and spatially resolved mid-circuit measurements, we identify dynamical heterogeneity --- a hallmark of glassy dynamics. We quantify this emergent behavior by studying the probability of finding inactive space-time regions of a given size. This quantity displays a crossover from an area- to perimeter-dominated scaling, which is a characteristic property of glasses and is associated with the proximity to a dynamical first-order phase transition. Our results establish current noisy intermediate-scale quantum devices as scalable testbeds for investigating correlated many-body phenomena at the level of measurement trajectories. Comments: Subjects: Quantum Physics (quant-ph); Statistical Mechanics (cond-mat.stat-mech) Cite as: arXiv:2608.19335 [quant-ph]   (or arXiv:2608.19335v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2608.19335 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Marcel Cech [view email] [v1] Wed, 19 Aug 2026 18:00:39 UTC (1,507 KB) Full-text links: Access Paper: View a PDF of the pa

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AI Coding Assistants are Getting Smarterquantum-computing

AI Coding Assistants are Getting Smarter

AI Coding Assistants are Getting Smarter By Doug Finke Last year, we published an article titled Quantum SDKs are Dying, Long Live Quantum AI SDK describing how the classical computing concept of “Vibe Coding” is entering the quantum programming space. (Perhaps we should call it Vibe Qoding!) We continue to see this trend accelerate and believe it will profoundly impact the future use of quantum computing technology. Workforce development remains a major concern within the quantum community. The central question is simple: How can we train thousands of potential users to program large-scale quantum systems within a reasonable timeframe? At industry conferences, speakers often ask, “If we put a 1-million-qubit quantum computer online next week, would anyone know how to program it and take advantage of its capabilities?” At GQI, we believe vibe coding will help solve this bottleneck. Placing these powerful AI tools directly into users’ hands will significantly shorten the time required to develop, test, and run quantum programs to solve real-world problems. Most industry experts we speak with agree: AI-assisted quantum coding will be the primary way people program quantum computers by 2030. The recent developments we describe below reinforce this outlook. The AI Decryption Optimization Race In April, we published The Decryption Threshold — Re-estimating the Quantum Threat to Blockchain Infrastructure covering a Google whitepaper on breaking the secp256k1 cryptographic algorithm. Google achieved this using a quantum computer an order of magnitude smaller than previously thought possible—requiring only 1,200–1,450 logical qubits and 70–90 million Toffoli gates. Because secp256k1 powers Bitcoin’s public-key cryptography and digital signatures, breaking it would have severe consequences. Today’s quantum hardware isn’t quite powerful enough yet, but GQI expects capable machines to arrive within the next few years. While we believe that Google manually developed its algorit

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